The results for Experiment 2 demonstrate that StyleRank is capable of making fine-grained distinctions between MIDI files that correspond with human perception of stylistic similarity. It is worth noting that participants found it difficult to discriminate between generated and human-composed samples in the BachBot experiment, evidenced by the average classification accuracy of novice (0.57), intermediate (0.64), advanced (0.68), and expert (0.71) participants [1]}. Based on our experimental results, the jSymbolic [2]} feature set is no better at predicting rankings than a random model. This is likely due to the fact that high level features are not sufficiently discriminative for this task. In contrast to the jSymbolic feature set, our method involves full categorical distributions, which we believe are critical in measuring fine-grained differences. Importantly, there is a substantial difference between the accuracy of rankings based on log-likelihood and StyleRank. Interestingly, both log-likelihood and StyleRank best model high certainty \((\alpha = 0.005)\)  comparisons made by self identified novices. This may be an artifact of increased variance as the number of ground truth comparisons decreases as \(\alpha \)  increases.
